Development HealthCare System of Smart Hospital Based on UML and XML

The convergence of information technology systems in health care system building is causing us to look at more effective integration of technologies. Facing increased competition, tighter spaces, staff retention and reduced reimbursement, today’s traditional hospitals are looking at strategic ways to use technology to manage their systems called smart hospital. The concept of the smart hospital is a useful system for any hospital; about adding intelligence to the traditional hospital system by covering all resources and locations with patient information. Patient’s information is an important component of the patient privacy in any health care system that is based on the overall quality of each patient in the health care system. The main commitment for any health care system is to improve the quality of the patient and privacy of patient’s information. Today, there is a need of such computer environment where treatment to patients can be given on the basis of his/her previous medical history at the time of emergency at any time, on any place and anywhere. Pervasive and ubiquitous environment and UML (unified modeling language) can bring the boon in this field. For this it's needed to develop the ubiquitous health care computing environment using the UML with traditional hospital environment. This paper is based on the ubiquitous and pervasive computing environment based on UML and XML(The Extensible Markup Language)  technology, in which these problems has been tried to improve traditional hospital system into smart hospital in the near future. The key solution of the smart hospital is online identification of all patients, doctors, nurses, staff, medical equipments, medications, blood bags, surgical tools, blankets, sheets, hospital rooms, etc. In this paper efforts is channeled into improving the knowledge-base ontological description for smart hospital system by using UML and XML technology, Our knowledge is represented in XML format from UML modeling(class diagram). Our smart hospital provides access to its system by using a smart card. Finally, the former try to improve health care delivery through development and management of acute care hospital designed; both physically and operationally, for more efficiency and increased patients safety. Keywords: UML; Smart Hospital (SH); Ontology; XML; health care system

An ambient agent architecture exploiting automated cognitive analysis

In this paper an agent-based ambient agent architecture is presented based on monitoring human's interaction with his or her environment and performing cognitive analysis of the causes of observed or predicted behaviour. Within this agent architecture, a cognitive model for the human is taken as a point of departure. From the cognitive model it is automatically derived how internal cognitive states affect human's performance aspects. Furthermore, for these cognitive states representation relations are derived from the cognitive model, expressed by temporal specifications involving events that will be monitored. The representation relations are verified on the monitoring information automatically, resulting in the identification of cognitive states, which affect the performance aspects. In such a way the ambient agent model is able to provide a more in depth cognitive analysis of causes of (un)satisfactory performance and based on this analysis to generate interventions in a knowledgeable manner. The application of the architecture proposed is demonstrated by two examples from the ambient-assisted living domain and the computer-assisted instruction domain. © 2011 The Author(s)

Ein modulares Konzept von Klassifikatoren für Aktivitätserkennung auf Mobiltelefonen

In this thesis a modular activity recognition using accelerometer sensors on mobile phones is presented, which includes solutions to five challenges: 1.Flexibility: The conditions of the mobile phone usage and therefore for the activity recognition can always change. An activity recognition needs to flexibly adapt to this changes. 2.Extensibility: Different users have different demands of activities to be recognized. Only a small set of activities are performed by nearly every user. Therefore, the recognition needs to be extensible to the individual needs. 3.Robustness: The device is typically not firmly attached to any position, which results in noisy sensor data. A robust recognition is needed, which is able to detect the activities with high accuracy. 4.Resources: The resources on mobile phones are limited (processor and battery capacity), therrfore the activity recognition needs not to have a high impact on these. 5.Conditionality: The user and her phone can be situated in various different conditions. Each of these conditionalities implies different sensor patterns, which need representation in the activity recognition algorithm. The modularity of the proposed approach enables the individual adaption of parts of the activity recognition to offer flexibility. A modular recognition is extensible by new modules which detect new activities. The recurrence of the classification process stabilizes the recognition and enables the derivation of a reliability measure. Only one module and not the whole activity recognition is active at each point in time, which decreases the calculation effort and therefore the energy consumption. Each module can be suited for dealing with one conditionality, through which neither the complexity of the recognition is increased nor the accuracy is significantly lowered. All these solutions to the challenges of activity recognition on mobile phones are rounded by a service, which supports the novel system on the common user's phone.In dieser Dissertation wird eine modulare Aktivitätserkennung mit Beschleunigungssensoren auf Mobiltelefonen vorgestellt, die Lösungen für folgende fünf Herausforderungen bereitstellt: 1.Flexibilität: Die Bedingungen der Nutzung eines Mobiltelefons und damit auch für die Aktivitätserkennung können sich jederzeit ändern. Eine Aktivitätserkennung muss flexibel auf diese Veränderungen reagieren können. 2.Erweiterbarkeit: Unterschiedliche Anwender haben unterschiedliche Anforderungen welche Aktivitäten erkannt werden sollen. Daher muss die Erkennung erweiterbar sein, um die individuellen Bedürfnisse befriedigen zu können. 3.Robustheit: Das Gerät ist typischerweise nicht fest an einer Position angebracht, woraus verrauschten Sensordaten resultieren. Desswegen ist eine robuste Erkennung erforderlich, welche in der Lage ist die Aktivitäten trotzdem mit hoher Genauigkeit zu detektieren. 4.Resources: Die Ressourcen (Prozessor und Akku-Kapazität) auf Handys sind beschränkt, weshalb die Aktivitätserkennung diese nicht noch zusätzlich übermäßig einschränken sollte. 5.Konditionalität: Der Benutzer und sein Telefon können in verschiedensten Gegebenheiten situiert sein. Jede dieser Konditionen impliziert andere Muster der Sensoren, welche jeweils durch die Aktivitätserkennung repräsentiert sein müssen. Durch die Modularität, welche in dieser Dissertation zur Bewältigung der Herausforderungen vorgeschlagen wird, wird ermöglicht, dass Flexibilität bereitgestellt werden kann. Eine modulare Erkennung ist erweiterbar durch neue Module, welche neue Aktivitäten erkennen. Die Rekurrenz des Klassifikationsprozesses stabilisiert die Erkennung. Nur ein Modul ist zu einem Zeitpunkt aktiv, was Ressourcen schont. Jedes Modul kann passend sein, um mit einer Konditionalität umzugehen, wobei die Komplexität weder erheblich erhöht noch die Genauigkeit stark erniedrigt wird. Alle diese Lösungen für die Herausforderungen der Aktivitätserkennung werden durch einen speziellen Service abgerundet